Differential diagnosis value of single-case statistical parametric mapping analysis with 18F-FDG PET imaging for Parkinsonism / 中华核医学与分子影像杂志
Chinese Journal of Nuclear Medicine and Molecular Imaging
; (6): 331-336, 2019.
Article
in Zh
| WPRIM
| ID: wpr-805432
Responsible library:
WPRO
ABSTRACT
Objective@#To investigate the value of statistical parametric mapping (SPM) analysis of 18F-fluorodeoxyglucose (FDG) PET imaging in the differential diagnosis of Parkinsonism in single-case level.@*Methods@#SPM software was used to retrospectively analyze the 18F-FDG PET images of 160 patients (104 males, 56 females, age: 30-82 years) who were suspected with Parkinsonism at baseline and were clinical confirmed by follow-up from April 2010 to December 2017. 18F-FDG PET images of patients was compared with those of age-matched healthy controls in single-case level using two-sample t test in SPM software to obtain the imaging diagnosis. By comparing imaging diagnosis with the final clinical diagnosis, the diagnostic accuracy of SPM in the overall cohort as well as the early subcohort (duration of disease less than 2 years (56 males, 22 females, age: 50-82 years)) were calculated respectively.@*Results@#Among 160 patients with Parkinsonism, 146(91.2%) had the same 18F-FDG PET diagnosis as their final clinical diagnosis. The diagnostic sensitivity for Parkinson′s disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and cortical basal ganglia degeneration (CBD) were 93.5%(86/92), 92.3%(24/26), 84.0%(21/25) and 15/17, respectively. The specificity were 95.6%(65/68), 95.5%(128/134), 96.3%(130/135) and 100%(143/143), respectively. In the early subcohort, the analysis also achieved similar differential diagnosis effectiveness(92.3%).@*Conclusion@#The single-case 18F-FDG PET imaging SPM analysis can be helpful in the early differential diagnosis of Parkinsonism effectively.
Full text:
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Index:
WPRIM
Type of study:
Diagnostic_studies
Language:
Zh
Journal:
Chinese Journal of Nuclear Medicine and Molecular Imaging
Year:
2019
Type:
Article